Keeping in view the usefulness of spatial information to achieve improved classification accuracies, the work [1]} proposed a spectral-spatial LSTM based network that learns spectral and spatial features of HSI by utilizing two separate LSTM followed softmax layer for classification, while a decision fusion strategy is implemented to get joint spectral-spatial classification results. Similarly, [2]} proposed a patch-based RNN with LSTM cells that incorporate multi-temporal and multi-spectral information along with spatial characteristics for land cover classification.
